def main(_):
hparams = train_mask_gan.create_hparams()
log_dir = FLAGS.base_directory
tf.gfile.MakeDirs(FLAGS.output_path)
output_file = tf.gfile.GFile(os.path.join(FLAGS.output_path, 'output.txt'),
mode='w')
# Load data set.
raw_data = spec_loader.spec_raw_data(FLAGS.data_dir)
train_data, valid_data = raw_data
# Generating more data on train set.
if FLAGS.sample_mode == SAMPLE_TRAIN:
data_set = train_data
elif FLAGS.sample_mode == SAMPLE_VALIDATION:
data_set = valid_data
else:
raise NotImplementedError
# Dictionary and reverse dictionry.
word_to_id = spec_loader.build_vocab()
id_to_word = {v: k for k, v in word_to_id.items()}
FLAGS.vocab_size = len(id_to_word)
print('Vocab size: %d' % FLAGS.vocab_size)
generate_samples(hparams, data_set, id_to_word, log_dir, output_file)
def main(_):
hparams = train_mask_gan.create_hparams()
log_dir = FLAGS.base_directory
tf.gfile.MakeDirs(FLAGS.output_path)
output_file = tf.gfile.GFile(
os.path.join(FLAGS.output_path, 'reviews.txt'), mode='w')
# Load data set.
if FLAGS.data_set == 'ptb':
raw_data = ptb_loader.ptb_raw_data(FLAGS.data_dir)
train_data, valid_data, _, _ = raw_data
elif FLAGS.data_set == 'imdb':
raw_data = imdb_loader.imdb_raw_data(FLAGS.data_dir)
train_data, valid_data = raw_data
else:
raise NotImplementedError
# Generating more data on train set.
if FLAGS.sample_mode == SAMPLE_TRAIN:
data_set = train_data
elif FLAGS.sample_mode == SAMPLE_VALIDATION:
data_set = valid_data
else:
raise NotImplementedError
# Dictionary and reverse dictionry.
if FLAGS.data_set == 'ptb':
word_to_id = ptb_loader.build_vocab(
os.path.join(FLAGS.data_dir, 'ptb.train.txt'))
elif FLAGS.data_set == 'imdb':
word_to_id = imdb_loader.build_vocab(
os.path.join(FLAGS.data_dir, 'vocab.txt'))
id_to_word = {v: k for k, v in word_to_id.iteritems()}
FLAGS.vocab_size = len(id_to_word)
print('Vocab size: %d' % FLAGS.vocab_size)
generate_samples(hparams, data_set, id_to_word, log_dir, output_file)